Overview

Dataset statistics

Number of variables11
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory278.3 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 2 other fieldsHigh correlation
recency_days is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with avg_ticketHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362)Skewed
frequency is highly skewed (γ1 = 24.88049136)Skewed
qtde_returns is highly skewed (γ1 = 51.79774426)Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
qtde_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-08-25 12:45:12.394658
Analysis finished2023-08-25 12:45:19.938812
Duration7.54 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:19.979342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-08-25T09:45:20.041340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.103736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2023-08-25T09:45:20.163048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.227090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2023-08-25T09:45:20.290000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.357487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2023-08-25T09:45:20.418360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_items
Real number (ℝ)

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.8525
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.480318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578
Coefficient of variation (CV)3.6594891
Kurtosis465.99808
Mean1608.8525
Median Absolute Deviation (MAD)422
Skewness17.858591
Sum4776683
Variance34663575
MonotonicityNot monotonic
2023-08-25T09:45:20.545786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
84 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
1200 7
 
0.2%
516 7
 
0.2%
Other values (1661) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.897762
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.612745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.956587
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9344
Coefficient of variation (CV)19.98033
Kurtosis2890.7071
Mean51.897762
Median Absolute Deviation (MAD)5.984842
Skewness53.444224
Sum154084.45
Variance1075233
MonotonicityNot monotonic
2023-08-25T09:45:20.672498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-67.348511
Minimum-366
Maximum-1
Zeros0
Zeros (%)0.0%
Negative2969
Negative (%)100.0%
Memory size46.4 KiB
2023-08-25T09:45:20.736779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-366
5-th percentile-201
Q1-85.333333
median-48.285714
Q3-25.923077
95-th percentile-8
Maximum-1
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)-0.94352388
Kurtosis4.8871091
Mean-67.348511
Median Absolute Deviation (MAD)26.285714
Skewness-2.0627709
Sum-199957.73
Variance4037.958
MonotonicityNot monotonic
2023-08-25T09:45:20.799948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 25
 
0.8%
-4 22
 
0.7%
-70 21
 
0.7%
-7 20
 
0.7%
-35 19
 
0.6%
-49 18
 
0.6%
-46 17
 
0.6%
-21 17
 
0.6%
-11 17
 
0.6%
-42 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
-366 1
 
< 0.1%
-365 1
 
< 0.1%
-363 1
 
< 0.1%
-362 1
 
< 0.1%
-357 2
0.1%
-356 1
 
< 0.1%
-355 2
0.1%
-352 1
 
< 0.1%
-351 2
0.1%
-350 3
0.1%
ValueCountFrequency (%)
-1 16
0.5%
-1.5 1
 
< 0.1%
-2 13
0.4%
-2.5 1
 
< 0.1%
-2.601398601 1
 
< 0.1%
-3 15
0.5%
-3.321428571 1
 
< 0.1%
-3.330357143 1
 
< 0.1%
-3.5 2
 
0.1%
-4 22
0.7%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.866393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049450549
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.03311068

Descriptive statistics

Standard deviation0.40815625
Coefficient of variation (CV)3.5866953
Kurtosis989.36508
Mean0.1137973
Median Absolute Deviation (MAD)0.012191338
Skewness24.880491
Sum337.8642
Variance0.16659153
MonotonicityNot monotonic
2023-08-25T09:45:20.929389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:20.997164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-08-25T09:45:21.063276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:21.133068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2023-08-25T09:45:21.198851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
136 8
 
0.3%
208 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.484591
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-08-25T09:45:21.266405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.142857
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47619

Descriptive statistics

Standard deviation15.460307
Coefficient of variation (CV)0.8842247
Kurtosis29.317441
Mean17.484591
Median Absolute Deviation (MAD)6.6
Skewness3.4358615
Sum51911.752
Variance239.02111
MonotonicityNot monotonic
2023-08-25T09:45:21.332178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
14 38
 
1.3%
17 38
 
1.3%
5 36
 
1.2%
11 36
 
1.2%
7 36
 
1.2%
15 35
 
1.2%
Other values (896) 2589
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-08-25T09:45:19.168942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:12.525084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.357667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.963126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.649817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.235579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.872841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.471439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.175137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.797865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.438056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.222046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:12.640183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.412654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.017709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.701551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.292073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.925703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.526192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.229973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.853927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.493785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.275008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:12.761325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.464392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.071813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.752556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.346638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.977366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.659834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.283984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.909549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.549905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.330517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:12.829247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.520103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.128212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.806758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.405941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.032991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.718208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.341898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.968910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.608867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.380630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:12.879516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.570343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.179124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.854648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.459072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.083184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.770302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.394507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.023491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.662708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.439126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.008053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.628882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.239351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.911212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.519792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.141423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.830654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.453936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.084585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.724009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.490986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.061056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.681465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.292647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.961662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.574893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.192082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.884940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.508462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.139908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.779533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.547559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.117701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.738254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.350672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.016746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.635084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.248176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.942189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.566612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.199969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.838580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.603234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.176079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.794775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.409025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.071972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.694774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.304261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.000365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.623874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.259695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.898593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.661635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.239565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.852548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.468814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.128244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.755693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.361761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.060234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.683110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.319965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.959742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.719874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.302539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:13.911006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:14.595854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.185374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:15.817379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:16.419215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.120575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:17.743535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:18.381698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-25T09:45:19.019719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-25T09:45:21.389796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0760.0010.026-0.070-0.131-0.019-0.002-0.063-0.123-0.016
gross_revenue-0.0761.000-0.4150.7700.9250.2460.2470.0900.3720.5740.104
recency_days0.001-0.4151.000-0.502-0.4080.048-0.1080.018-0.120-0.0980.015
qtde_invoices0.0260.770-0.5021.0000.7160.0590.2590.0790.2940.100-0.181
qtde_items-0.0700.925-0.4080.7161.0000.1670.2270.0800.3440.7290.147
avg_ticket-0.1310.2460.0480.0590.1671.0000.1220.0900.1900.188-0.618
avg_recency_days-0.0190.247-0.1080.2590.2270.1221.0000.8810.3960.077-0.130
frequency-0.0020.0900.0180.0790.0800.0900.8811.0000.2340.027-0.121
qtde_returns-0.0630.372-0.1200.2940.3440.1900.3960.2341.0000.210-0.054
avg_basket_size-0.1230.574-0.0980.1000.7290.1880.0770.0270.2101.0000.402
avg_unique_basket_size-0.0160.1040.015-0.1810.147-0.618-0.130-0.121-0.0540.4021.000

Missing values

2023-08-25T09:45:19.795888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-25T09:45:19.889846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.018.152222-35.50000017.00000040.050.9705880.617647
1130473232.5956.09.01390.018.904035-27.2500000.02830235.0154.44444411.666667
2125836705.382.015.05028.028.902500-23.1875000.04032350.0335.2000007.600000
313748948.2595.05.0439.033.866071-92.6666670.0179210.087.8000004.800000
415100876.00333.03.080.0292.000000-8.6000000.07317122.026.6666670.333333
5152914623.3025.014.02102.045.326471-23.2000000.04011529.0150.1428574.357143
6146885630.877.021.03621.017.219786-18.3000000.057221399.0172.4285717.047619
7178095411.9116.012.02057.088.719836-35.7000000.03352041.0171.4166673.833333
81531160767.900.091.038194.025.543464-4.1444440.243316474.0419.7142866.230769
9160982005.6387.07.0613.029.934776-47.6666670.0243900.087.5714294.857143
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
4269177271060.2515.01.0645.016.064394-6.01.0000006.0645.00000066.000000
427717232421.522.02.0203.011.708889-12.00.1538460.0101.50000015.000000
427817468137.0010.02.0116.027.400000-4.00.4000000.058.0000002.500000
428113596697.045.02.0406.04.199036-7.00.2500000.0203.00000066.500000
4286148931237.859.02.0799.016.956849-2.00.6666670.0399.50000036.000000
429012479473.2011.01.0382.015.773333-4.01.00000034.0382.00000030.000000
430514126706.137.03.0508.047.075333-3.00.75000050.0169.3333334.666667
4309135211092.391.03.0733.02.511241-4.50.3000000.0244.333333104.000000
431415060301.848.04.0262.02.515333-1.02.0000000.065.50000020.000000
431912558269.967.01.0196.024.541818-6.01.000000196.0196.00000011.000000